| 水循环模拟与水资源配置云模型服务平台构建与应用 |
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| 引用本文： | 赵晶,高峰,王涛,毕彦杰,段晶晶.水循环模拟与水资源配置云模型服务平台构建与应用[J].农业工程学报,2023,39(15):293-304. |
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| 作者姓名： | 赵晶 高峰 王涛 毕彦杰 段晶晶 |
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| 作者单位： | 华北水利水电大学 水资源学院, 郑州 450046;中水北方勘测设计研究有限责任公司, 天津 300022 |
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| 基金项目： | 国家自然科学基金项目（52009042） |
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| 摘 要： | 水利云模型研究仍处于起步阶段，缺乏水循环模拟、水资源配置云模型服务平台。该研究构建水循环模型、水资源多目标优化配置模型，采用模块化建模方式，以模型库为核心，基于B/S开发架构，设计研发多模型云服务平台，实现两个模型的双向耦合，并以渭河流域陕西段为例，构建渭河流域陕西段水循环模拟与水资源多目标配置云模型服务平台，模拟分析研究区水循环演变规律，开展规划水平年水资源多目标优化配置研究。结果表明：1)状头以上率定期和验证期的相对误差分别为-4.32%和-1.62%，月平均径流Nash-Sutcliffe效率系数分别为0.80和0.82，林家村—咸阳率定期和验证期的相对误差分别为-4.96%和-1.71%，月平均径流Nash-Sutcliffe效率系数分别为0.79和0.81，云模型服务平台的水循环模型较好地刻画了径流变化过程；2)调用云模型服务平台的水资源配置模型，利用遗传算法(NSGA)-Ⅲ、群体多属性决策模型筛选出渭河流域水资源合理配置方案，2025年需水量63.35亿m3，供水量为58.30亿m3，缺水5.05亿m3，缺水...
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| 关 键 词： | 水资源 模型 水循环 多目标优化配置 云模型平台 双向耦合 渭河流域陕西段 |
| 收稿时间： | 2022/9/23 0:00:00 |
| 修稿时间： | 2023/2/14 0:00:00 |
Construction and application of the cloud model service platform for water cycle simulation and water resources configuration |
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| ZHAO Jing,GAO Feng,WANG Tao,BI Yanjie,DUAN Jingjing.Construction and application of the cloud model service platform for water cycle simulation and water resources configuration[J].Transactions of the Chinese Society of Agricultural Engineering,2023,39(15):293-304. |
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| Authors: | ZHAO Jing GAO Feng WANG Tao BI Yanjie DUAN Jingjing |
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| Institution: | College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450046, China;Bei Fang Investigation, Design & Research CO. LTD, Tianjin 300222, China |
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| Abstract: | A cloud model of water resources has been explored in the early stages in recent years. However, it is still lacking on the cloud model service platforms for the water cycle simulation and water resources allocation. This study aims to construct the water cycle models for water resource allocation using multi-objective optimization. A modular modeling approach was also adopted with the model library as the core. The B/S architecture pattern was utilized to configure the basic network structure. The cloud model service platform was designed using the classic cloud computing architecture. Four layers were divided into: IaaS (Infrastructure as a Service), PaaS (Platform as a Service), SaaS (Software as a Service), and Visualization Service Layer. The calculation services were provided by the water cycle model and water resources optimization model (SaaS layer) on the server side. The core of the entire system included the solving methods, numerical models, file systems, and database components. The backend database selected MySQL to support data for the entire system, including graphical, modeling, calculation, and hydrological data. Two parts were on the browser side: 1) Modeling and flow field display module included the numerical model building, parameter setting, simulation statistics and querying, and flow field display; 2) Data transmission and calls between the browser and Web server was achieved via the Python interface in HTML, enabling the seamless data exchange. Once an external request was initiated from the browser, it was sent to the server via the Python interface. Subsequently, the Python logic on the server side invoked Python models to perform analysis and calculations using the received request. The computed data from the models was then presented within the HTML framework through the server, indicating the seamless integration and display of the calculation on the user interface. Taking the Shaanxi section of the Weihe River Basin as an example, the new platform was used to simulate the water cycle evolution after multi-objective optimization for the water resources allocation in the long-term planning. The results show that: 1) The relative errors of monthly average runoff were -4.32% and -1.02%, respectively, in the periods of calibration and verification. The Nash-Sutcliffe efficiency coefficients of monthly average runoff were 0.82 and 0.85, respectively. It infers that the water cycle model of the cloud model service platform was effectively characterized by the runoff during the verification period. The total rainfall and the total evaporation values were 21.78, and 17.97 billion cubic meters, respectively, under the multiple-year average inflow frequency in 2025. The average depth of annual runoff was 121.80 mm with a total runoff volume of 5.63 billion cubic meters. The inflow and outflow water volume values were 3.39, and 7.20 billion cubic meters, respectively. The total water withdrawal was 5.10 billion cubic meters for economic and social purposes, where 2.767, 2.369, and 0.40 billion cubic meters were from the surface water, shallow groundwater, and deep groundwater, respectively. The water outputs in the Shaanxi Province of the Wei River Basin were evaporation and socio-economic water withdrawal, accounting for 77.89% and 22.11%, respectively. The spatial distribution of runoff depth depended mainly on the distribution of rainfall. A significant decreasing trend was also found in the runoff volume at various hydrological stations. 2) The Pareto optimal frontier was obtained using the NSGA-III (Non-dominated Sorting Genetic Algorithm III). An optimal plan of water resources allocation was obtained using a population multi-attribute decision-making model, where five decision-makers were assumed to participate in the decision-making. As such, the water shortage rate, grain yield reduction rate, and GDP reduction were 7.95%, 1.23%, and 0.15%, respectively, in the entire region in 2025. The agricultural water deficit reached 438 million cubic meters, accounting for 87.04% of the total water deficit. Water shortages were concentrated mainly in the areas from Baoji Gorge to Xianyang North, Xianyang to Tongguan South, and Xianyang to Tongguan North. The findings can provide technical references and insights for the smart water conservancy system and the refined allocation of water resources in the Weihe River Basin. |
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| Keywords: | water resources models water cycle allocation multi objective optimization configuration cloud model platform bidirectional coupling Shaanxi section of the Weihe River Basin |
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